When Does Cryptographic Reference Integrity Add Value? Threat Conditions in the Verification of AI Recovery

RT5 asks when protected reference records improve decisions about AI recovery. It separates record availability, authenticity, freshness, measurement validity, and decision cost, and shows why failure depth alone cannot determine cryptographic value. A reproducible audit compares six decision policies across 24 fixed record fixtures. Signatures with an independently retained checkpoint expose several alterations, while withheld evidence can require abstention and authentic but initially false measurements can still mislead. These are protocol and counterexample checks rather than measurements of AI recovery. The contribution is a threat-conditioned assessment design; deployment benefit and universal depth-based prescriptions are not established.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-10
DOI
https://doi.org/10.5281/zenodo.22683885
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

When Does Cryptographic Reference Integrity Add Value? Threat Conditions in the Verification of AI Recovery

Bin Seol
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

When Does Cryptographic Reference Integrity Add Value? Threat Conditions in the Verification of AI Recovery

Bin Seol
article en

Abstract

RT5 asks when protected reference records improve decisions about AI recovery. It separates record availability, authenticity, freshness, measurement validity, and decision cost, and shows why failure depth alone cannot determine cryptographic value. A reproducible audit compares six decision policies across 24 fixed record fixtures. Signatures with an independently retained checkpoint expose several alterations, while withheld evidence can require abstention and authentic but initially false measurements can still mislead. These are protocol and counterexample checks rather than measurements of AI recovery. The contribution is a threat-conditioned assessment design; deployment benefit and universal depth-based prescriptions are not established.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Adversarial Robustness in Machine Learning
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When Does Cryptographic Reference Integrity Add Value? Threat Conditions in the Verification of AI Recovery — Bin Seol · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS